Multimedia Derived Wind Map

ABSTRACT

Embodiments of the present invention disclose a method, a computer program product, and a computer system for generating a wind map based on multimedia analysis. A computer receives a multimedia source configuration and builds a wind scale reference database. In addition, the computer extracts and processes both wind speed data and contextual data from the multimedia. Moreover, the computer analyses temporal and spatial features, as well as generates a wind map based on the extracted context, extracted wind speed, and analysed temporal and spatial features. Lastly, the wind map generator validates and modifies the wind scale reference database.

BACKGROUND

The present invention relates generally to wind mapping, and moreparticularly to wind mapping using multimedia data.

Wind is moving air caused by a difference in air pressure within theatmosphere. Near-surface wind data is closely related to the everydaybusiness life, for example at wind energy farms and airports, as well aspersonal life, for example sailing and flying a kite. For these reasons,wind is closely monitored, however, variables in the atmosphericboundary layer are complex. Wind is particularly complex due to beingtransient, heterogeneous, and greatly impacted by the complex surfacefeatures, such as terrain elevation and bodies of water. While wind datacollection has traditionally relied on measurements from wind towers,anemometers, weather vanes, weather balloons, remote sensing techniques,and the like, these measurements are sparse and the instruments areexpensive to both purchase and maintain.

SUMMARY

Embodiments of the present invention disclose a method, a computerprogram product, and a computer system for generating a wind map basedon multimedia analysis. In embodiments herein, the present inventiondiscloses deducing a wind speed of wind identified in one or moremultimedia based on a wind scale reference database, extractingcontextual data corresponding to the one or more multimedia, andgenerating a wind map based on the deduced wind speed and extractedcontextual data.

According to some embodiments, extracting the contextual datacorresponding to the one or more multimedia further comprises extractinga geolocation wherein the one or more multimedia was recorded,extracting a date and a time at which the one or more multimedia wasrecorded, and extracting an elevation of the wind recorded in the one ormore multimedia.

In accordance with some embodiments, the present invention furthercomprises retrieving a verified wind speed from a weather station withina threshold spatial proximity and a threshold temporal proximity to theextracted geolocation, comparing the verified wind speed to the deducedwind speed, and modifying the wind scale reference database based on thecomparison.

Moreover, some embodiments may further comprise removing false positivemultimedia from the one or more multimedia by applying natural languageprocessing to the extracted contextual data.

According to an embodiment of the present invention, the one or moremultimedia includes at least one of text, audio, image, and video. Inaddition, extracting the contextual data corresponding to the one ormore multimedia further comprises transcribing the audio, applyingkeyword searching and natural language processing to the transcribedaudio and the text, and applying image recognition technology to theimage and the video.

In various embodiments, the present invention may further compriseextracting temporal and spatial features from the multimedia, whereinthe temporal and spatial features include terrain and topographic wind,building winds, city winds, heat island effects, lake effects, andmountain waves. Moreover, deducing the wind speed of the wind is furtherbased on the extracted temporal and spatial features.

According to some embodiments, extracting the contextual datacorresponding to the one or more multimedia further comprises extractinga wind direction from the one or more multimedia, extracting anatmospheric pressure from the one or more multimedia, and extracting atemperature from the one or more multimedia.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the invention solely thereto, will best be appreciatedin conjunction with the accompanying drawings, in which:

FIG. 1 depicts a schematic diagram of a wind map generating system 100,in accordance with an embodiment of the present invention.

FIG. 2 depicts a flowchart illustrating the operations of a wind mapgenerator 124 of the wind map generating system 100 in deriving a windmap via multi-source analysis.

FIG. 3 depicts the wind scale reference database 122 of FIG. 1, inaccordance with an embodiment of the present invention.

FIG. 4 illustrates a block diagram depicting the hardware components ofthe wind map generating system 100 of FIG. 1, in accordance with anexample embodiment of the present invention.

FIG. 5 depicts a cloud computing environment, in accordance with anembodiment of the present invention.

FIG. 6 depicts abstraction model layers, in accordance with anembodiment of the present invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention. In the drawings, like numbering representslike elements.

DETAILED DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to implement such feature, structure, orcharacteristic in connection with other embodiments whether or notexplicitly described.

In the interest of not obscuring the presentation of embodiments of thepresent invention, in the following detailed description, someprocessing steps or operations that are known in the art may have beencombined together for presentation and for illustration purposes and insome instances may have not been described in detail. In otherinstances, some processing steps or operations that are known in the artmay not be described at all. It should be understood that the followingdescription is focused on the distinctive features or elements ofvarious embodiments of the present invention.

FIG. 1 depicts a wind map generating system 100, in accordance withembodiments of the present invention. In the example embodiment, windmap generating system 100 includes a client device 110 and a server 120,interconnected via a network 108. While, in the example embodiment,programming and data of the present invention are stored and accessedremotely across several servers via the network 108, in otherembodiments, programming and data of the present invention may be storedlocally on as few as one physical computing device or amongst othercomputing devices than those depicted.

In the example embodiment, the network 108 is a communication channelcapable of transferring data between connected devices. In the exampleembodiment, the network 108 is the Internet, representing a worldwidecollection of networks and gateways to support communications betweendevices connected to the Internet. Moreover, the network 108 mayinclude, for example, wired, wireless, and/or fiber optic connectionswhich may be implemented as an intranet network, a local area network(LAN), a wide area network (WAN), or a combination thereof. In furtherembodiments, the network 108 may be a Bluetooth network, a WiFi network,or a combination thereof. In yet further embodiments, the network 108may be a telecommunications network used to facilitate telephone callsbetween two or more parties comprising a landline network, a wirelessnetwork, a closed network, a satellite network, or a combinationthereof. In general, the network 108 can be any combination ofconnections and protocols that will support communications betweenconnected devices.

In the example embodiment, the client device 110 may be an enterpriseserver, a laptop computer, a notebook, a tablet computer, a netbookcomputer, a personal computer (PC), a desktop computer, a server, apersonal digital assistant (PDA), a rotary phone, a touchtone phone, asmart phone, a mobile phone, a virtual device, a thin client, or anyother electronic device or computing system capable of receiving andsending data to and from other computing devices. While the clientdevice 110 is shown as a single device, in other embodiments, the clientdevice 110 may be comprised of a cluster or plurality of computingdevices, working together or working separately. Similarly, while FIG. 1illustrates only one instance of the client device 110 in the interestof brevity, in other embodiments, any number of instances of the clientdevice 110 may be implemented within the wind map generating system 100.In the example embodiment, the client device 110 includes anaccelerometer, a gyroscope, a barometer, an altimeter, a means forconnecting to a global positioning system (GPS), and other integratedsensors for collecting information regarding an environment. The clientdevice 110 is described in greater detail with reference to FIG. 4.

In the example embodiment, the server 120 includes a wind scalereference database 122 as well as a wind map generator 124, and may bean enterprise server, a laptop computer, a notebook, a tablet computer,a netbook computer, a personal computer (PC), a desktop computer, aserver, a personal digital assistant (PDA), a rotary phone, a touchtonephone, a smart phone, a mobile phone, a virtual device, a thin client,or any other electronic device or computing system capable of receivingand sending data to and from other devices. While the server 120 isshown as a single device, in other embodiments, the server 120 may becomprised of a cluster or plurality of computing devices, workingtogether or working separately. The server 120 is described in greaterdetail with reference to FIG. 4.

In the example embodiment, the wind scale reference database 122 is arelational database used for relating wind scale and speed to observableatmospheric events and other phenomena. More specifically, the windscale reference database 122 correlates a wind scale and correspondingwind speed with descriptions of phenomena found in multimedia.Accordingly, the wind scale reference database 122 is populated withkeywords, descriptions, target objects, phenomena, and other indicatorsof wind found within multimedia that are associated with a particularwind scale and wind speed. The wind scale reference database 122, inaccordance with an embodiment of the present invention, is depicted byFIG. 3 and described in greater detail with reference to FIG. 2.

The wind map generator 124 is a software program capable of receiving amultimedia source configuration and building a wind scale referencedatabase. In addition, the wind map generator 124 is capable ofextracting and processing both wind speed data and contextual data fromthe multimedia. Moreover, the wind map generator 124 is capable ofanalysing temporal and spatial features, as well as generating a windmap based on the extracted context, extracted wind speed, and analysedtemporal and spatial features. Lastly, the wind map generator 124 iscapable of validating and modifying the wind scale reference database.

FIG. 2 illustrates the operations of the wind map generator 124 of thewind map generating system 100 in generating a wind map throughmultimedia analysis.

The wind map generator 124 receives a source data configuration (step202). In the example embodiment, the wind map generator 124 isconfigured to monitor various sources from which data relating to windspeed may be extracted and processed in order to ultimately derive awind speed. Accordingly, the wind map generator 124 is configured tocrawl structured and unstructured multimedia, e.g., text, audio, images,video, etc., of preconfigured sources such as websites, databases,applications, and other digital media for particular keywords,expressions, reference objects, events/phenomena, and other featuresindicative of a current wind speed. Such sources may include, forexample, social collaboration websites, messaging platforms, digitalmedia sharing platforms, personal/professional networking platforms,digital libraries, multimedia repositories, news and entertainmentmultimedia, sports multimedia, etc.

To further illustrate the operations of the wind map generator 124,reference is now made to an illustrative example where the wind mapgenerator 124 is configured to search a social media platform, amessaging application, and an image sharing application for variousforms of multimedia.

The wind map generator 124 builds the wind scale reference database 122(step 204). In the example embodiment, the wind scale reference database122 is a relational database used for relating wind scale and speed toobservable atmospheric events and other phenomena. More specifically,the wind map generator 124 determines a wind scale and correspondingwind speed by comparing phenomena observed in various multimedia sourceswith descriptions and keywords of phenomena detailed by the wind scalereference database 122. Accordingly, the wind scale reference database122 is populated with keywords, descriptions, target objects, phenomena,and other indicators of wind found within multimedia that are associatedwith a particular wind scale and wind speed. An example wind scalereference database 122 is illustrated in FIG. 3 where Wind Scale andWind Speed are correlated with various, observable atmosphericconditions, namely a Wind Description, a Wave Height at sea, one or moreSea Observations, and one or more Land Observations. For example, andwith reference to FIG. 3, observing “ripples without crests” at sea isan indicator of a Wind Scale of 1 and wind speeds of 1-3 mph (1-5 km/h).In the example embodiment, such wind scales may be custom made and/orbased on existing and known wind scales, such as the Beaufort Wind Scaleand scales offered by the World Meteorological Organization (WMO)Commission for Instruments and Methods of Observation (CIMO). Moreover,the wind scales and wind scale reference database may be continuouslymodified and improved through use of a feedback loop, described ingreater detail below.

Referring now to the previously introduced illustrative example, thewind map generator 124 builds the wind scale reference database relatingwind speed and scale to observable atmospheric conditions depicted byFIG. 3. Here, the wind scale reference database 122 correlates a windscale and speed to various observable phenomena on land and at sea.

The wind map generator 124 extracts and processes wind speed data fromthe source multimedia (step 206). In the example embodiment, the windmap generator 124 identifies multimedia having an indication of wind anddeduces a corresponding wind scale by searching multimedia sources forobservables detailed by the wind scale reference database 122, forexample those listed under the columns entitled Wave Height, SeaObservations, and Land Observations (FIG. 3). The wind map generator 124identifies such observables by searching the multimedia for keywords andtarget objects that are described by the wind scale reference database122, then uses a matching algorithm to determine an appropriate windscale value. For example, and with reference to FIG. 3, the wind mapgenerator 124 may be configured to associate audio or text mentioning“flat” or “calm” as they relate to waves with a wind scale of 0. Inaddition, and again with reference to FIG. 3, the wind map generator 124may be configured to associate an image/video of smoke rising verticallywith a wind scale of 0.

In the example embodiment (step 206 continued), the wind map generator124 may be configured to search the multimedia for all observableswithin the wind scale reference database 122 and determine anappropriate wind scale using various matching algorithm techniques. Inembodiments where observables from multiple wind scales are extracted,for example, the wind map generator 124 may default to the highest windscale having observables or the wind scale having the most observablesidentified in multimedia. In an example illustrating the former, if thewind map generator 124 identifies two observables corresponding to awind scale of 3 and one observable corresponding to a wind scale of 4,then the wind map generator 124 defaults to the wind scale of 4.Alternatively, and exemplifying the latter situation, if the wind mapgenerator 124 identifies two observables corresponding to a wind scaleof 4 and one observable corresponding to a wind scale of 5, then thewind map generator 124 defaults to the wind scale of 5. In otherembodiments, the wind map generator 124 may be configured to weightparticular observables based on a feedback loop (described in greaterdetail below) and average a weighted score of the observables for eachwind scale before defaulting to the greatest weighted score. In yetfurther embodiments, the wind map generator 124 may be configured toaverage wind scales when multiple wind scales are involved usingsubscales, for example a scale of 3.5 in the aforementioned example.

It is important to note that wind is transient and variable, andmoreover the present application cannot dictate when the multimedia fromwhich it deduces a wind speed is captured. Thus, in order to account forchanges over time, such observations may be broken down or consolidatedbased on threshold time blocks, for example, by the second, minute,hour, or day. For example, wind speeds deduced from three differentmultimedia recorded at a similar time and place may be averaged over oneminute to provide a single, averaged wind speed. The rate at whichdeduced wind speeds are averaged may be varied by geographic location,time, availability, multimedia supply, and other variables. For example,the rates at which deduced times are averaged may be more frequent in acity, where multimedia is frequently captured, than in a rural town withless residents and less captured multimedia.

Similarly, the present invention cannot dictate where multimedia iscaptured and, like above, may break down or consolidate observationsbased on a threshold radius. For example, wind speeds deduced within aspecific distance of each other may be averaged such that the wind mapgenerator 124 provides a single, average wind speed for the entire area.The threshold distance at which deduced wind speeds are averaged may bevaried by geographic location, time, availability, multimedia supply,and other variables. For example, the distances at which deduced speedsare averaged may be lesser and more granular in a city, where multimediais frequently captured, than in a rural town with less residents andless captured multimedia.

The wind map generator 124 extracts and processes contextual data fromthe source multimedia (step 208). Having extracted a deduced wind speed,the wind map generator 124 now extracts contextual information withwhich to associate with the deduced wind speed. In the exampleembodiment, the wind map generator 124 utilizes the contextual data inorder to pinpoint a place, date/time, elevation of the wind, winddirection, pressure, temperature, and other contextual informationregarding each deduced wind speed in order to generate a comprehensivewind map. Accordingly, the wind map generator 124 extracts and processescontextual data that includes data relating to a geolocation at whichthe multimedia was recorded, a date/time at which the multimedia wasrecorded, wind elevation, wind direction, atmospheric pressure,temperature, and other contextual data relevant to the wind map. Inorder to extract the contextual data, the wind map generator 124analyses structured data associated with the multimedia, for examplemetadata such as timestamps, hashtags, geotags, as well as unstructureddata associated with the multimedia, for example natural language speechand objects/phenomena depicted by the multimedia. The followingparagraphs describe how the wind map generator 124 extracts andprocesses the contextual data from various multimedia.

In order to extract a geolocation at which the multimedia was recorded(step 208 continued), the wind map generator 124 analyses a geotagassociated with metadata of the source multimedia. In the example above,for instance, the wind map generator 124 inspects metadata associatedwith an image on the image sharing platform to identify a geotagindicating that the image was taken in New York City (NYC). In furtherembodiments, the wind map generator 124 may extract a geolocation byapplying image recognition and optical character recognition techniquesto images and videos in order to identify particular objects, naturalformations, landmarks, buildings, restaurants, license plates, streetsigns, text, and the like. As such, the wind map generator 124 may beconfigured to recognize location-based objects and indicators such assnow or currency, natural formations such as the Grand Canyon andNiagara Falls, landmarks such as statues, parks, and buildings, and textsuch as that depicted on buildings, menus, and street signs. Again, withreference to the illustrative example, the wind map generator 124 isconfigured to identify a geolocation of NYC based on identifying theEmpire State Building in the foreground of a captured video. Whenapplicable, the wind map generator 124 may be further configured tocross reference the extracted objects, natural formations, landmarks,and text with various online resources to determine/verify a preciselocation where the multimedia was captured. For example, the wind mapgenerator 124 may identify the precise location of a particularrestaurant, park, or street corner mentioned within/depicted by themultimedia by searching for it within an online reference database ormap.

In yet further embodiments (step 208 continued), the wind map generator124 may be configured to extract a geolocation by implementing keywordsearches in conjunction with natural language processing to text andaudio transcribed to text. For example, the wind map generator 124 maytranscribe recorded conversations and search them for particularkeywords such as restaurants, parks, streets, and cities. In theillustrative example above, for instance, the wind map generator 124 mayextract a geolocation based on identifying the keywords “5^(th) andMadison” within a video recording . Moreover, upon identification of akeyword, the wind map generator 124 may be configured to apply naturallanguage processing techniques to the text in order to filter out falsepositives, such as hypothetical plans to visit a location orprevious/future trips to the location. With reference again to theearlier introduced example, the wind map generator 124 applies naturallanguage processing to filter a false positive mention of NYC whereinthe multimedia text reads “I hear the wind makes it feel very cold inNew York City during the winter” because the reference to wind in NYCwas with regard to the winter, and not current wind context.

In addition (step 208 continued), the wind map generator 124 extractsand processes a date and time at which the multimedia was recorded.Similar to the manner in which the wind map generator 124 extracts ageotag from multimedia, the wind map generator 124 extracts a date/timeat which the multimedia was recorded by analysing a timestamp associatedwith metadata corresponding to the multimedia. As applied to theillustrative example, for instance, the wind map generator 124 extractsa timestamp from metadata of an image depicting a flag waving in thewind. In further embodiments, the wind map generator 124 may extract atime of capture by applying image recognition and optical characterrecognition techniques to images/videos in order to identify particularobjects, phenomena, and text depicted by the multimedia. For example,the wind map generator 124 may be configured to recognize time-basedobjects such as a clock or watch, phenomena such as a sunrise, noon, orsunset, and text such as that depicted on street signs, buildings, andmenus. In the previously introduced example, for instance, the wind mapgenerator 124 extracts a time of capture from an image depicting a trainschedule having a digital clock. In addition, the wind map generator 124may extract a time of capture from a picture depicting the sun settingover a horizon.

When applicable (step 208 continued), the wind map generator 124 may befurther configured to cross reference the extracted objects, phenomena,and text with various online sources and other contextual data todetermine/verify a precise time at which the multimedia was captured.For example, the wind map generator 124 may search a website for dinnerhours of a restaurant depicted in a dinner photo, or times of a sportingevent depicted in the background of a photo. With reference to theprevious example having multimedia depicting a setting sun, the wind mapgenerator 124 may extract a time of capture by cross referencing asunset time for that particular geolocation. In yet further embodiments,the wind map generator 124 may be configured to extract a time ofcapture by implementing keyword searches in conjunction with naturallanguage processing to text and audio transcribed to text. For example,the wind map generator 124 may transcribe recorded conversations andsearch them for particular keywords corresponding to time, such asreferences to seconds/minutes/hours, appointments, and time-relevantevents such as breakfast, lunch, and happy hour. In the example above,for instance, the wind map generator 124 extracts a time mentioned in arecorded video.

In embodiments (step 208 continued), and upon detection of a particularkeyword, the wind map generator 124 may additionally apply naturallanguage processing techniques to the text in order to filter out falsepositives, such as hypothetical plans for a particular time and/orprevious/future references to time. For instance, the wind map generator124 may filter out video based on determining that a mention to time waswith reference to a future business meeting and not the time ofrecording. In yet further embodiments, the wind map generator 124 mayextract times from audio recordings that include events such aschurch/bell tower ringing at prescribed times and/or transportationannouncements at bus stations, train stations, and airports. In theexample above, for instance, if a multimedia is recorded in GrandCentral Train Station and an announcement over a loud speaker indicatesa scheduled train is boarding, the wind map generator 124 identifies thescheduled train departure time.

Furthermore (step 208 continued), the wind map generator 124 processesand extracts data relating to an elevation of wind identified within themultimedia. In the example embodiment, the wind map generator 124 isconfigured to appreciate that wind speed is subject to elevation andsurface obstruction, and therefore estimates an elevation of wind basedon one or more objects with known size, referred to herein as elevationreference objects. In order to extract an elevation of observed wind,the wind map generator 124 first associates elevation reference objects,such as people, objects (i.e., flag poles, trees, buildings/roofs,etc.), and the like with predetermined levels of elevation. For example,the wind map generator 124 may associate the elevation reference objectof an adult male human with an elevation of an adult male human in thatparticular geographic region. Similarly, the wind map generator 124 maybe configured to associate a height of stairs/floors of a building witha predetermined elevation, and associate those elevations to eachrecognizable stair/floor of a building.

After establishing one or more elevation reference objects in multimedia(step 208 continued), the wind map generator 124 then utilizes thepredetermined size of the reference object in order to deduce anelevation of wind relative to the elevation reference object. Thisprocess comprises comparing the elevation reference object to an objectfrom which wind is deduced, referred to herein as a wind referenceobject. For example, if the elevation reference object is a flag polewith known height and the wind reference object is a flag at the top ofthe flag pole, then the wind map generator 124 identifies the windelevation as the same as the elevation reference object (flag pole) andthe wind reference object (flag). Alternatively, if the wind mapgenerator 124 determines that the flag is at half-staff, then the windmap generator 124 identifies the wind elevation and wind referenceobject relative (half) as that of the elevation reference object.

Similarly (step 208 continued), the wind map generator 124 may furtherprocess and extract a wind direction from the multimedia. In the exampleembodiment, the wind map generator 124 may deduce a wind direction usingsimilar techniques to those above in identifying other contextual data.In order to extract a direction of observed wind, the wind map generator124 first associates objects with known direction, referred to herein asdirectional reference objects, such as compasses, maps, roads/highways,landmarks, statues, buildings, the sun, etc., with predetermineddirections. For example, the wind map generator 124 may associate an“avenue” in Manhattan with north and south directions and a “street” inManhattan with east and west directions. Similarly, the wind mapgenerator 124 may associate a sun appearing in the morning with theeastern direction, and the face of a building with the southerndirection Like the extractions above, the wind map generator 124 may befurther configured to cross-reference the extracted location, date/time,and/or elevation of the multimedia with an identified directionalreference object. For example, if the geolocation of a photo indicatesthat it was taken on 42^(nd) street in NYC, the wind map generator 124may reference a map database to determine that 42^(nd) street runs eastand west. In addition, the wind map generator 124 may further crossreference the geolocation with objects identified within the photo, suchas buildings, parks, restaurants, traffic, roads, etc. to establishwhich direction of 42^(nd) street is east/west.

Moreover (step 208 continued), in addition to geolocation, the wind mapgenerator 124 may further consider a time of capture in determining winddirection. For example, if the photo includes the sun and was taken at6:10 AM in NYC, then the wind map generator 124 may deduce that the sunmust be depicted as easterly in the image. Furthering the example, ifthe photo was taken in January, when the sun pans more southerly from aperspective in the northern hemisphere, the wind map generator 124 maybe additionally configured to associate the sun in a more southeastdirection than it would during mid-summer when the sun rises moreeasterly. After establishing a directional reference within themultimedia, the wind map generator 124 may then determine a winddirection relative to the established, known direction(s). In otherembodiments, for example those involving text and audio, the wind mapgenerator 124 may transcribe and process the text using keywordsearching techniques to identify terms indicative of direction, e.g.,north, south, east, west, etc., and further apply natural languageprocessing to filter out false positives, not unlike the methods usedabove.

When applicable (step 208 continued), the client device 110 may beconfigured to include additional metadata stamps within a multimedia,allowing for additional data extraction. For example, the client device110 may utilize a built-in altimeter and/or barometer on capture and addelevation and/or pressure to metadata of captured multimedia. Suchmeasurements may be used to further refine deduced wind speeds and mayfurther be associated with particular wind scales within the wind scalereference database 122.

The wind map generator 124 analyses temporal and spatial features (step210). Similar to the manner in which the present invention appreciateselevation as a factor in deducing wind speed, the wind map generator 124additionally considers temporal and spatial features relevant to windspeed. In the example embodiment, temporal and spatial features refer toknow and unknown terrain, sea, terrain-sea transition, and time basedphenomena that relate to wind patterns. Such features include terrainand topographic wind (e.g., wind over crests of hills/ridges,escarpments, vegetation, etc.), building winds, city winds, heat islandeffects, lake effects, mountain waves, and the like. Such temporal andspatial features may be used to identify, validate, and further predictwind speeds within the generated wind speed map. For example, repeatedobservations of unusually high wind speeds between buildings in a citymay be an indicator of an unknown pattern of building winds, or mayverify an already-known pattern of building wind. Moreover, suchpatterns may be further considered for additional research or as afactor in deducing wind speed, and may even be used in predicting futurewinds.

The wind map generator 124 generates a wind map (step 212). In theexample embodiment, the wind map generator 124 generates a wind mapdetailing a deduced wind speed at various locations at a given time. Itwill be appreciated that, depending on an amount of multimediaavailable, it may be impractical to display a deduced wind speed foreach and every multimedia identified, particularly in highly populatedareas. As such, it may be desirable to decrease (or increase) an amountof displayed wind speeds by consolidating (e.g., averaging) or otherwisemodifying wind speeds for a specific location at a specific time. Suchmodification may be based on variables such as location, population, anamount of multimedia available, wind variability, application, demand,and the like. In addition to modifying the displayed wind speed based onlocation, the wind map generator 124 may further increase, decrease, orotherwise modify time frames in which wind speeds are observed and, insome embodiments, average deduced speeds over those time frames. Likethe modification of wind speeds across a location, above, such timeframe modification may be based on location, population, an amount ofmultimedia available, wind variability, application, demand, and thelike.

Accordingly (step 212 continued), the generated wind map may vary inscope for a particular location at particular times. Alternatively, thegenerated wind map may be consolidated into a complete wind map for anentire region, however may incorporate interactive functions. Forexample, a zoomed out version of the map may display deduced speeds at alarger granularity that averages the wind speeds within a particularradius based on various metrics, for example a maximum amount oflocations per prescribed area or an aspect ratio of locations to windowsize. In addition, zooming in on a particular area may increase locationand wind granularity such that the wind speeds that were averaged whilezoomed out are now broken down into discrete locations. Moreover, theinteractive wind map may further include a duration slider whichconsolidates, for example via averaging, deduced wind speeds over agiven time frame, for example the last five minutes or hour. Inembodiments, the generated map may further include previously deducedand, in some embodiments, corrected wind speeds. Finally, the wind mapmay be further configured to predict future wind speeds at givenlocations based on previously identified patterns of the wind speed, forexample based on location, time, and weather conditions.

The wind map generator 124 validates the wind scale reference database(step 214). In the example embodiment, the wind map generator 124verifies that the wind speeds deduced from the multimedia are in factaccurate. In order to do this, the wind map generator 124 referenceshighly-accurate wind measuring devices positioned at various weatherstations in close spatial and temporal proximity to a location at whichspeed was deduced via multimedia. For example, if an image is capturedwithin fifty feet of a school having a local weather station, the windmap generator 124 compares the wind speed deduced from the image to themeasured wind speed sensed by the school weather station. Similarly, thewind map generator 124 may further reference radar and satellite imageryto compare measured wind speed to that deduced from multimedia in aclose geographic proximity. In yet further embodiments, deduced windspeeds may be extrapolated between known weather stations by applyingalgorithms and determined wind patterns.

The wind map generator 124 refines the wind scale reference database 122(step 216). In the example embodiment, the wind map generator 124utilizes the previous validation step in order to modify the wind scalereference database 122 accordingly. In addition, the wind map generator124 may further refine the keywords and target objects described by thewind scale reference database 122, as well as thresholds and identifyingfeatures associated with the wind scale reference database 122. Forexample, and with reference to FIG. 3, the wind map generator 124 mayadjust the angle at which smoke need be detected as rising in order tobe considered indicative of a wind scale of 1 rather than 0 (“smokerises vertically”). Similarly, the wind map generator 124 may modify anamount of white foam that need be identified, for example through pixelanalysis, in order to warrant a wind scale of 6.

In addition (step 216 continued), the wind map generator 124 may beconfigured to add, remove, and reassign keywords, target objects, etc.,associated with each of the wind scales within the wind scale referencedatabase 122. For example, if the keyword “strong breeze” isconsistently used in multimedia to refer to a measured wind scale of 7,as opposed to a scale of 6 as indicted by the wind scale referencedatabase 122, then the wind map generator 124 may reassign the keyword“strong breeze” to wind scale 7. The wind map generator 124 may befurther configured to add observables to the wind scale referencedatabase 122, such as keywords, expressions, idioms, and other phraseshaving not only syntactic matches to wind-based terminology but semanticmatches as well. For example, if the wind map generator 124 identifiesmultimedia mentioning wind in the context of “raining sideways”, thenthe wind map generator 124 cross references “raining sideways” todetermine whether the phrase has semantic wind meaning. If the wind mapgenerator 124 determines that a phrase, idiom, expression, etc. containssemantic wind meaning, in this case indicating that the wind is sostrong it is fighting gravity, then the wind map generator 124determines which scale to associate the phrase with in a similar mannerto the validation step above. Specifically, the wind map generator 124may associate the expression, phrase, etc., with one or more verifiedwind speeds within spatial and temporal proximity to the multimedia. Inother embodiments, the wind map generator 124 may determine a wind scalewith which to associate newly identified phrases based on other factors,including natural language processing, reference to additionaldatabases, user input, and, among others, combinations of the above. Thewind map generator 124 then adds the newly identified phrase to the windscale reference database 122, thereby increasing observables by which todeduce speed while increasing performance and accuracy of the wind mapgenerator 124.

FIG. 3 depicts an example wind scale reference database, in accordancewith an embodiment of the present invention.

FIG. 4 depicts a block diagram of client device 110 and server 120 ofthe wind map generating system 100 of FIG. 1, in accordance with anembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Client device 110 may include one or more processors 02, one or morecomputer-readable RAMs 04, one or more computer-readable ROMs 06, one ormore computer readable storage media 08, device drivers 12, read/writedrive or interface 14, network adapter or interface 16, allinterconnected over a communications fabric 18. Communications fabric 18may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 10, and one or more application programs11, for example interaction prediction program 142, are stored on one ormore of the computer readable storage media 08 for execution by one ormore of the processors 02 via one or more of the respective RAMs 04(which typically include cache memory). In the illustrated embodiment,each of the computer readable storage media 08 may be a magnetic diskstorage device of an internal hard drive, CD-ROM, DVD, memory stick,magnetic tape, magnetic disk, optical disk, a semiconductor storagedevice such as RAM, ROM, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Client device 110 may also include a R/W drive or interface 14 to readfrom and write to one or more portable computer readable storage media26. Application programs 11 on said devices may be stored on one or moreof the portable computer readable storage media 26, read via therespective R/W drive or interface 14 and loaded into the respectivecomputer readable storage media 08.

Client device 110 may also include a network adapter or interface 16,such as a TCP/IP adapter card or wireless communication adapter (such asa 4G wireless communication adapter using OFDMA technology). Applicationprograms 11 on said computing devices may be downloaded to the computingdevice from an external computer or external storage device via anetwork (for example, the Internet, a local area network or other widearea network or wireless network) and network adapter or interface 16.From the network adapter or interface 16, the programs may be loadedonto computer readable storage media 08. The network may comprise copperwires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers.

Client device 110 may also include a display screen 20, a keyboard orkeypad 22, and a computer mouse or touchpad 24. Device drivers 12interface to display screen 20 for imaging, to keyboard or keypad 22, tocomputer mouse or touchpad 24, and/or to display screen 20 for pressuresensing of alphanumeric character entry and user selections. The devicedrivers 12, R/W drive or interface 14 and network adapter or interface16 may comprise hardware and software (stored on computer readablestorage media 08 and/or ROM 06).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 40 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 40 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes40 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and wind observation processing 96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1. A computer-implemented method for generating a wind map based onmultimedia analysis, the method comprising: deducing a wind speed ofwind identified in one or more multimedia based on a wind scalereference database; extracting contextual data corresponding to the oneor more multimedia; and generating a wind map based on the deduced windspeed and extracted contextual data.
 2. The computer-implemented methodof claim 1, wherein extracting the contextual data corresponding to theone or more multimedia further comprises: extracting a geolocationwherein the one or more multimedia was recorded; extracting a date and atime at which the one or more multimedia was recorded; and extracting anelevation of the wind recorded in the one or more multimedia.
 3. Thecomputer-implemented method of claim 2, further comprising: retrieving averified wind speed from a weather station within a threshold spatialproximity and a threshold temporal proximity to the extractedgeolocation; comparing the verified wind speed to the deduced windspeed; and modifying the wind scale reference database based on thecomparison.
 4. The computer-implemented method of claim 1, furthercomprising: removing false positive multimedia from the one or moremultimedia by applying natural language processing to the extractedcontextual data.
 5. The computer-implemented method of claim 1, whereinthe one or more multimedia includes at least one of text, audio, image,and video; and wherein extracting the contextual data corresponding tothe one or more multimedia further comprises: transcribing the audio;applying keyword searching and natural language processing to thetranscribed audio and the text; and applying image recognitiontechnology to the image and the video.
 6. The computer-implementedmethod of claim 1, further comprising: extracting temporal and spatialfeatures from the multimedia, wherein the temporal and spatial featuresinclude terrain and topographic wind, building winds, city winds, heatisland effects, lake effects, and mountain waves; and wherein deducingthe wind speed of the wind is further based on the extracted temporaland spatial features.
 7. The computer-implemented method of claim 2,wherein extracting the contextual data corresponding to the one or moremultimedia further comprises: extracting a wind direction from the oneor more multimedia; extracting an atmospheric pressure from the one ormore multimedia; and extracting a temperature from the one or moremultimedia.
 8. A computer program product for generating a wind mapbased on multimedia analysis, the computer program product comprising:one or more non-transitory computer-readable storage media and programinstructions stored on the one or more non-transitory computer-readablestorage media capable of performing a method, the method comprising:deducing a wind speed of wind identified in one or more multimedia basedon a wind scale reference database; extracting contextual datacorresponding to the one or more multimedia; and generating a wind mapbased on the deduced wind speed and extracted contextual data.
 9. Thecomputer program product of claim 8, wherein extracting the contextualdata corresponding to the one or more multimedia further comprises:extracting a geolocation wherein the one or more multimedia wasrecorded; extracting a date and a time at which the one or moremultimedia was recorded; and extracting an elevation of the windrecorded in the one or more multimedia.
 10. The computer program productof claim 9, further comprising: retrieving a verified wind speed from aweather station within a threshold spatial proximity and a thresholdtemporal proximity to the extracted geolocation; comparing the verifiedwind speed to the deduced wind speed; and modifying the wind scalereference database based on the comparison.
 11. The computer programproduct of claim 8, further comprising: removing false positivemultimedia from the one or more multimedia by applying natural languageprocessing to the extracted contextual data.
 12. The computer programproduct of claim 8, wherein the one or more multimedia includes at leastone of text, audio, image, and video; and wherein extracting thecontextual data corresponding to the one or more multimedia furthercomprises: transcribing the audio; applying keyword searching andnatural language processing to the transcribed audio and the text; andapplying image recognition technology to the image and the video. 13.The computer program product of claim 8, further comprising: extractingtemporal and spatial features from the multimedia, wherein the temporaland spatial features include terrain and topographic wind, buildingwinds, city winds, heat island effects, lake effects, and mountainwaves; and wherein deducing the wind speed of the wind is further basedon the extracted temporal and spatial features.
 14. The computer programproduct of claim 9, wherein extracting the contextual data correspondingto the one or more multimedia further comprises: extracting a winddirection from the one or more multimedia; extracting an atmosphericpressure from the one or more multimedia; and extracting a temperaturefrom the one or more multimedia.
 15. A computer system for generating awind map based on multimedia analysis, the computer system comprising:one or more computer processors, one or more computer-readable storagemedia, and program instructions stored on one or more of thecomputer-readable storage media for execution by at least one of the oneor more processors capable of performing a method, the methodcomprising: deducing a wind speed of wind identified in one or moremultimedia based on a wind scale reference database; extractingcontextual data corresponding to the one or more multimedia; andgenerating a wind map based on the deduced wind speed and extractedcontextual data.
 16. The computer system of claim 15, wherein extractingthe contextual data corresponding to the one or more multimedia furthercomprises: extracting a geolocation wherein the one or more multimediawas recorded; extracting a date and a time at which the one or moremultimedia was recorded; and extracting an elevation of the windrecorded in the one or more multimedia.
 17. The computer system of claim16, further comprising: retrieving a verified wind speed from a weatherstation within a threshold spatial proximity and a threshold temporalproximity to the extracted geolocation; comparing the verified windspeed to the deduced wind speed; and modifying the wind scale referencedatabase based on the comparison.
 18. The computer system of claim 15,further comprising: removing false positive multimedia from the one ormore multimedia by applying natural language processing to the extractedcontextual data.
 19. The computer system of claim 15, wherein the one ormore multimedia includes at least one of text, audio, image, and video;and wherein extracting the contextual data corresponding to the one ormore multimedia further comprises: transcribing the audio; applyingkeyword searching and natural language processing to the transcribedaudio and the text; and applying image recognition technology to theimage and the video.
 20. The computer system of claim 15, furthercomprising: extracting temporal and spatial features from themultimedia, wherein the temporal and spatial features include terrainand topographic wind, building winds, city winds, heat island effects,lake effects, and mountain waves; and wherein deducing the wind speed ofthe wind is further based on the extracted temporal and spatialfeatures.